README.md

([简体中文](./README_cn.md)|English)
# Speech Verification)

## Introduction

Speaker Verification, refers to the problem of getting a speaker embedding from an audio. 

This demo is an implementation to extract speaker embedding from a specific audio file. It can be done by a single command or a few lines in python using `PaddleSpeech`. 

## Usage
### 1. Installation
see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).

You can choose one way from easy, meduim and hard to install paddlespeech.

### 2. Prepare Input File
The input of this demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.

Here are sample files for this demo that can be downloaded:
```bash
wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
```

### 3. Usage
- Command Line(Recommended)
  ```bash
  paddlespeech vector --task spk --input 85236145389.wav

  echo -e "demo1 85236145389.wav" > vec.job
  paddlespeech vector --task spk --input vec.job

  echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk
  ```
  
  Usage:
  ```bash
  paddlespeech asr --help
  ```
  Arguments:
  - `input`(required): Audio file to recognize.
  - `model`: Model type of asr task. Default: `conformer_wenetspeech`.
  - `sample_rate`: Sample rate of the model. Default: `16000`.
  - `config`: Config of asr task. Use pretrained model when it is None. Default: `None`.
  - `ckpt_path`: Model checkpoint. Use pretrained model when it is None. Default: `None`.
  - `device`: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.

  Output:

```bash
  demo [ -5.749211     9.505463    -8.200284    -5.2075014    5.3940268
  -3.04878      1.611095    10.127234   -10.534177   -15.821609
   1.2032688   -0.35080156   1.2629458  -12.643498    -2.5758228
 -11.343508     2.3385992   -8.719341    14.213509    15.404744
  -0.39327756   6.338786     2.688887     8.7104025   17.469526
  -8.77959      7.0576906    4.648855    -1.3089896  -23.294737
   8.013747    13.891729    -9.926753     5.655307    -5.9422326
 -22.842539     0.6293588  -18.46266    -10.811862     9.8192625
   3.0070958    3.8072643   -2.3861165    3.0821571  -14.739942
   1.7594414   -0.6485091    4.485623     2.0207152    7.264915
  -6.40137     23.63524      2.9711294  -22.708025     9.93719
  20.354511   -10.324688    -0.700492    -8.783211    -5.27593
  15.999649     3.3004563   12.747926    15.429879     4.7849145
   5.6699696   -2.3826702   10.605882     3.9112158    3.1500628
  15.859915    -2.1832209  -23.908653    -6.4799504   -4.5365124
  -9.224193    14.568347   -10.568833     4.982321    -4.342062
   0.0914714   12.645902    -5.74285     -3.2141201   -2.7173362
  -6.680575     0.4757669   -5.035051    -6.7964664   16.865469
 -11.54324      7.681869     0.44475392   9.708182    -8.932846
   0.4123232   -4.361452     1.3948607    9.511665     0.11667654
   2.9079323    6.049952     9.275183   -18.078873     6.2983274
  -0.7500531   -2.725033    -7.6027865    3.3404543    2.990815
   4.010979    11.000591    -2.8873312    7.1352735  -16.79663
  18.495346   -14.293832     7.89578      2.2714825   22.976387
  -4.875734    -3.0836344   -2.9999814   13.751918     6.448228
 -11.924197     2.171869     2.0423572   -6.173772    10.778437
  25.77281     -4.9495463   14.57806      0.3044315    2.6132357
  -7.591999    -2.076944     9.025118     1.7834753   -3.1799617
  -4.9401326   23.465864     5.1685796   -9.018578     9.037825
  -4.4150195    6.859591   -12.274467    -0.88911164   5.186309
  -3.9988663  -13.638606    -9.925445    -0.06329413  -3.6709652
 -12.397416   -12.719869    -1.395601     2.1150916    5.7381287
  -4.4691963   -3.82819     -0.84233856  -1.1604277  -13.490127
   8.731719   -20.778936   -11.495662     5.8033476   -4.752041
  10.833007    -6.717991     4.504732    13.4244375    1.1306485
   7.3435574    1.400918    14.704036    -9.501399     7.2315617
  -6.417456     1.3333273   11.872697    -0.30664724   8.8845
   6.5569253    4.7948146    0.03662816  -8.704245     6.224871
  -3.2701402  -11.508579  ]
  ```

- Python API
  ```python
  import paddle
  from paddlespeech.cli import VectorExecutor

  vector_executor = VectorExecutor()
  audio_emb = vector_executor(
      model='ecapatdnn_voxceleb12',
      sample_rate=16000,
      config=None, 
      ckpt_path=None,
      audio_file='./85236145389.wav',
      force_yes=False,
      device=paddle.get_device())
  print('Audio embedding Result: \n{}'.format(audio_emb))
  ```

  Output:
  ```bash
  # Vector Result:
  [ -5.749211     9.505463    -8.200284    -5.2075014    5.3940268
  -3.04878      1.611095    10.127234   -10.534177   -15.821609
   1.2032688   -0.35080156   1.2629458  -12.643498    -2.5758228
 -11.343508     2.3385992   -8.719341    14.213509    15.404744
  -0.39327756   6.338786     2.688887     8.7104025   17.469526
  -8.77959      7.0576906    4.648855    -1.3089896  -23.294737
   8.013747    13.891729    -9.926753     5.655307    -5.9422326
 -22.842539     0.6293588  -18.46266    -10.811862     9.8192625
   3.0070958    3.8072643   -2.3861165    3.0821571  -14.739942
   1.7594414   -0.6485091    4.485623     2.0207152    7.264915
  -6.40137     23.63524      2.9711294  -22.708025     9.93719
  20.354511   -10.324688    -0.700492    -8.783211    -5.27593
  15.999649     3.3004563   12.747926    15.429879     4.7849145
   5.6699696   -2.3826702   10.605882     3.9112158    3.1500628
  15.859915    -2.1832209  -23.908653    -6.4799504   -4.5365124
  -9.224193    14.568347   -10.568833     4.982321    -4.342062
   0.0914714   12.645902    -5.74285     -3.2141201   -2.7173362
  -6.680575     0.4757669   -5.035051    -6.7964664   16.865469
 -11.54324      7.681869     0.44475392   9.708182    -8.932846
   0.4123232   -4.361452     1.3948607    9.511665     0.11667654
   2.9079323    6.049952     9.275183   -18.078873     6.2983274
  -0.7500531   -2.725033    -7.6027865    3.3404543    2.990815
   4.010979    11.000591    -2.8873312    7.1352735  -16.79663
  18.495346   -14.293832     7.89578      2.2714825   22.976387
  -4.875734    -3.0836344   -2.9999814   13.751918     6.448228
 -11.924197     2.171869     2.0423572   -6.173772    10.778437
  25.77281     -4.9495463   14.57806      0.3044315    2.6132357
  -7.591999    -2.076944     9.025118     1.7834753   -3.1799617
  -4.9401326   23.465864     5.1685796   -9.018578     9.037825
  -4.4150195    6.859591   -12.274467    -0.88911164   5.186309
  -3.9988663  -13.638606    -9.925445    -0.06329413  -3.6709652
 -12.397416   -12.719869    -1.395601     2.1150916    5.7381287
  -4.4691963   -3.82819     -0.84233856  -1.1604277  -13.490127
   8.731719   -20.778936   -11.495662     5.8033476   -4.752041
  10.833007    -6.717991     4.504732    13.4244375    1.1306485
   7.3435574    1.400918    14.704036    -9.501399     7.2315617
  -6.417456     1.3333273   11.872697    -0.30664724   8.8845
   6.5569253    4.7948146    0.03662816  -8.704245     6.224871
  -3.2701402  -11.508579  ]
  ```

### 4.Pretrained Models

Here is a list of pretrained models released by PaddleSpeech that can be used by command and python API:

| Model | Sample Rate
| :--- | :---: |
| ecapatdnn_voxceleb12 | 16k